Continuous fungal treatment of non-sterile veterinary hospital effluent: pharmaceuticals removal and microbial community assessment

Source point treatment of effluents with a high load of pharmaceutical active compounds (PhACs), such as hospital wastewater, is a matter of discussion among the scientific community. Fungal treatments have been reported to be successful in degrading this type of pollutants and, therefore, the white-rot fungus Trametes versicolor was applied for the removal of PhACs from veterinary hospital wastewater. Sixty-six percent removal was achieved in a non-sterile batch bioreactor inoculated with T. versicolor pellets. On the other hand, the study of microbial communities by means of DGGE and phylogenetic analyses led us to identify some microbial interactions and helped us moving to a continuous process. PhAC removal efficiency achieved in the fungal treatment operated in non-sterile continuous mode was 44 % after adjusting the C/N ratio with respect to the previously calculated one for sterile treatments. Fungal and bacterial communities in the continuous bioreactors were monitored as well.

Saprotrophic fungi Hypholoma fasciculare effect on the fungal community associated to Castanea sativa

Dissertação de mestrado em Biologia Molecular, Biotecnologia e Bioempreendedorismo em Plantas

Hydrocarbonoclastic bacteria: from bioremediation to bioenergy feedstock

Tese de Doutoramento em Engenharia Química e Biológica.

Effects of residues on the degradation of PHA produced from mixed microbial cultures and processed in extrusion

Thermal degradation upon melting is one of the major drawbacks reported for polyhydroxyalkanoates (PHA). However, the role of residues originating from the fermentation and the extraction steps on the thermal stability of this class of biopolymers still needs to be clarified. In the particular case of PHA produced from mixed microbial cultures (MMC), this topic is even less documented in the literature. Here, two polyhydroxy(butyrate-co-valerate) (PHBV) produced from MMC enriched in PHA accumulating organisms and fed with cheese whey were studied. A micro extrusion line is used to produce filaments and assess the processability and the degradation of processed PHBV. The prototype micro extrusion line allows for studying grams of materials. The two PHBV contain 18 mol% HV. PHBV was recovered with 11 wt% residues, and further submitted to a purification procedure resulting in a second biopolyester containing less than 2 wt% impurities. The thermorheological characterization of the two PHBV is first presented, together with their semicrystalline properties. Then the processing windows of the two biopolyesters are presented. Finally, the properties of extruded filaments are reported and the thermomechanical degradation of PHBV is extensively studied. The structure was assessed by wide angle X-ray diffraction, mechanical and rheological properties are reported, thermal properties are studied with differential scanning calorimetry and thermogravimetric analysis, whereas Fourier Transform Infrared spectroscopy was used to assess the impact of the extrusion on PHBV chemical structure. All results obtained with the two PHBV are compared to assess the effects of residues on both PHBV processability and degradation.

Quantitative image analysis as a tool for Yarrowia lipolytica dimorphic growth evaluation in different culture media

Yarrowia lipolytica, a yeast strain with a huge biotechnological potential, capable to produce metabolites such as γ-decalactone, citric acid, intracellular lipids and enzymes, possesses the ability to change its morphology in response to environmental conditions. In the present study, a quantitative image analysis (QIA) procedure was developed for the identification and quantification of Y. lipolytica W29 and MTLY40-2P strains dimorphic growth, cultivated in batch cultures on hydrophilic (glucose and N-acetylglucosamine (GlcNAc) and hydrophobic (olive oil and castor oil) media. The morphological characterization of yeast cells by QIA techniques revealed that hydrophobic carbon sources, namely castor oil, should be preferred for both strains growth in the yeast single cell morphotype. On the other hand, hydrophilic sugars, namely glucose and GlcNAc caused a dimorphic transition growth towards the hyphae morphotype. Experiments for γ-decalactone production with MTLY40-2P strain in two distinct morphotypes (yeast single cells and hyphae cells) were also performed. The obtained results showed the adequacy of the proposed morphology monitoring tool in relation to each morphotype on the aroma production ability. The present work allowed establishing that QIA techniques can be a valuable tool for the identification of the best culture conditions for industrial processes implementation.

Proteins on microbial identification: past achievements, present state-of-the-art, and future challenges

The use of chemical analysis of microbial components, including proteins, became an important achievement in the 80’s of the last century to the microbial identification. This led a more objective microbial identification scheme, called chemotaxonomy, and the analytical tools used in the field are mainly 1D/2D gel electrophoresis, spectrophotometry, high-performance liquid chromatography, gas chromatography, and combined gas chromatography-mass spectrometry. The Edman degradation reaction was also applied to peptides sequence giving important insights to the microbial identification. The rapid development of these techniques, in association with knowledge generated by DNA sequencing and phylogeny based on rRNA gene and housekeeping genes sequences, boosted the microbial identification to an unparalleled scale. The recent results of mass spectrometry (MS), like Matrix-Assisted Laser Desorption/Ionisation Time-of-Flight (MALDI-TOF), for rapid and reliable microbial identification showed considerable promise. In addition, the technique is rapid, reliable and inexpensive in terms of labour and consumables when compared with other biological techniques. At present, MALDI-TOF MS adds an additional step for polyphasic identification which is essential when there is a paucity of characters or high DNA homologies for delimiting very close related species. The full impact of this approach is now being appreciated when more diverse species are studied in detail and successfully identified. However, even with the best polyphasic system, identification of some taxa remains time-consuming and determining what represents a species remains subjective. The possibilities opened with new and even more robust mass spectrometers combined with sound and reliable databases allow not only the microbial identification based on the proteome fingerprinting but also include de novo specific proteins sequencing as additional step. These approaches are pushing the boundaries in the microbial identification field.

Effect of sulfate and iron (III) on LCFA degradation by a methanogenic community

[Excerpt] Under anaerobic conditions long chain fatty acids (LCFA) can be converted to methane by syntrophic bacteria and methanogenic archaea. LCFA degradation was also reported in the presence of alternative hydrogenotrophic partners, such as sulfate-reducing bacteria (SRB) and iron-reducing bacteria (IRB), which generally show higher affinity for H2 than methanogens and are more resistant to LCFA [1,2,3]. Their presence in a microbial culture degrading LCFA can be advantageous to reduce LCFA toxicity towards methanogens, although high concentrations of external electron acceptor (EEA) can lead to outcompetition of methanogens and cease methane production. In this work, we tested the effect of adding sub-stoichiometric concentrations of sulfate and iron(III) to methanogenic communities degrading LCFA. (...)

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

Effects of fermentation residues on the melt processability and thermomechanical degradation of PHBV produced from cheese whey using mixed microbial cultures

"Available online 28 March 2016"

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.